Backfill operations generally include operations that migrate data seamlessly between systems. For instance, when migrating from a legacy data processing system to a new state-of-the-art data processing system, the new system will be developed by engineers and existing data used by the legacy system will be backfilled or otherwise migrated to the new system such that the new system will become compatible and operational with existing data. End users may not be aware of the migration to the new system, as their user account data is retained and used by the new system.
Large-scale data processing systems such as web services and the like can produce vast amounts of log data including data generated by various end users, such as visitors of a network site and users of a mobile application. From time to time, it may be desirable to review such data to identify events of interest. For example, a marketing department may desire to identify behavioral patterns of individual users. However, the quantity of log data generated by such systems may present significant difficulties in terms of data storage and review. Querying data stores having millions to billions of entries, for example, may consume bandwidth, monopolize computing resources, and provide slow search results. Moreover, determining actions that influence how a consumer interacts with an application or service may be subjective and speculative.